In the field of reverse engineering, numerous techniques have been proposed to recover the software architecture from source code. These approaches aim to improve the accuracy and comprehensibility of the recovered architecture. However, a means of leveraging the recovered architectural structure to inform software quality issues, such as the location of defects, has not been explored. In the field of data mining, numerous approaches have been proposed to leverage co-change information in revision history to locate error-prone files, and construct defect predictors. There is no known methodology for directly and effectively linking software architecture with quality issues such as error-proneness.